The number of images, as well as other types of media content, that are available to users via their computers, especially with the evolvement of the Internet, has become very large and is continuing to grow daily. One significant problem faced given this large and dynamic set of images is how to effectively retrieve images from it that match certain search criteria.
One attempted solution to retrieve images has been a manual text-based or keyword-based solution, in which a human operator assigns to each image in the set one or more keywords describing the image. During the image retrieval process, the search criteria are compared to the keywords of images in the set of images, and an image with keywords that match the search criteria are returned. However, because of the human operator required to manually assign keywords, this process is particularly slow and subjective.
Another attempted solution to retrieve images and overcome these problems of manual keyword assignment has been to use content-based image retrieval, in which low-level features of the images are extracted (e.g., color histogram, texture, shape, etc.) and compared to corresponding search features to identify matches. However, the use of such low-level features can be fairly inaccurate because it is difficult for low-level features to represent high-level semantic content of the images (e.g., how do you get low-level features to represent “summer”?).
An additional problem faced with image retrieval is the ever-expanding (open) base of images. Images are continually being made available via the Internet, so successful solutions to image retrieval problems should be able to account for an ever-changing image base.
The invention described below addresses these disadvantages, providing media content searching exploiting related high-level text features and user log mining.